




'''simple docstring'''


import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def     SCREAMING_SNAKE_CASE							(   a_						:				Union[str, Any]					,       a_						:				bool = True					,       a_						:				float = math.inf					,       a_						:				float = -math.inf					,       a_						:				float = math.inf					,       a_						:				float = -math.inf					,       a_						:				bool = False					,       a_						:				float = 100					,       a_						:				float = 0.01					,       a_						:				float = 1					,       ):
  __a								=       False
  __a								=       search_prob
  __a								=       start_temperate
  __a								=       []
  __a								=       0
  __a								=       None

  while not search_end:
    __a								=       current_state.score()
    if best_state is None or current_score > best_state.score():
      __a								=       current_state
    scores.append(a_      )
    iterations += 1
    __a								=       None
    __a								=       current_state.get_neighbors()
    while (
        next_state is None and neighbors
    ):  # till we do not find a neighbor that we can move to
      __a								=       random.randint(0					,       len(a_      ) - 1      )  # picking a random neighbor
      __a								=       neighbors.pop(a_      )
      __a								=       picked_neighbor.score() - current_score

      if (
          picked_neighbor.x > max_x
          or picked_neighbor.x < min_x
          or picked_neighbor.y > max_y
          or picked_neighbor.y < min_y
      ):
        continue  # neighbor outside our bounds

      if not find_max:
        __a								=       change * -1  # in case we are finding minimum
      if change > 0:  # improves the solution
        __a								=       picked_neighbor
      else:
        __a								=       (math.e) ** (
            change / current_temp
        )  # probability generation function
        if random.random() < probability:  # random number within probability
          __a								=       picked_neighbor
    __a								=       current_temp - (current_temp * rate_of_decrease)

    if current_temp < threshold_temp or next_state is None:
      # temperature below threshold, or could not find a suitable neighbor
      __a								=       True
    else:
      __a								=       next_state

  if visualization:
    from matplotlib import pyplot as plt

    plt.plot(range(a_      )					,       a_      )
    plt.xlabel('Iterations'      )
    plt.ylabel('Function values'      )
    plt.show()
  return best_state


if __name__ == "__main__":


  def     SCREAMING_SNAKE_CASE							(   a_						:				Tuple					,       a_						:				Union[str, Any]      ):
    return (x**2) + (y**2)

  # starting the problem with initial coordinates (12, 47)
  UpperCAmelCase_					=   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  UpperCAmelCase_					=   simulated_annealing(
      prob, find_max=False, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
  )
  print(
      "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
      f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
  )

  # starting the problem with initial coordinates (12, 47)
  UpperCAmelCase_					=   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  UpperCAmelCase_					=   simulated_annealing(
      prob, find_max=True, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
  )
  print(
      "The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
      f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
  )


  def     SCREAMING_SNAKE_CASE							(   a_						:				Union[str, Any]					,       a_						:				str      ):
    return (3 * x**2) - (6 * y)

  UpperCAmelCase_					=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  UpperCAmelCase_					=   simulated_annealing(prob, find_max=False, visualization=True)
  print(
      "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
      f"""{local_min.score()}"""
  )

  UpperCAmelCase_					=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  UpperCAmelCase_					=   simulated_annealing(prob, find_max=True, visualization=True)

  print(
      "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
      f"""{local_min.score()}"""
  )









'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class  __lowercase    (	__magic_name__							,				__magic_name__							,				__magic_name__							,				unittest.TestCase			):
   _a    						=   StableUnCLIPImgaImgPipeline
   _a    						=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
   _a    						=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _a    						=   frozenset(
       []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   _a    						=   frozenset([]			)







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       32
     __a								=       embedder_hidden_size

     # image encoding components

     __a								=       CLIPImageProcessor(crop_size=32   ,				size=32   )

     torch.manual_seed(0   )
     __a								=       CLIPVisionModelWithProjection(
         CLIPVisionConfig(
             hidden_size=UpperCamelCase   ,				projection_dim=UpperCamelCase   ,				num_hidden_layers=5   ,				num_attention_heads=4   ,				image_size=32   ,				intermediate_size=37   ,				patch_size=1   ,				)   )

     # regular denoising components

     torch.manual_seed(0   )
     __a								=       StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase   )
     __a								=       DDPMScheduler(beta_schedule='squaredcos_cap_v2'   )

     torch.manual_seed(0   )
     __a								=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

     torch.manual_seed(0   )
     __a								=       CLIPTextModel(
         CLIPTextConfig(
             bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=UpperCamelCase   ,				projection_dim=32   ,				intermediate_size=37   ,				layer_norm_eps=1e-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)   )

     torch.manual_seed(0   )
     __a								=       UNetaDConditionModel(
         sample_size=32   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')   ,				up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')   ,				block_out_channels=(32, 64)   ,				attention_head_dim=(2, 4)   ,				class_embed_type='projection'   ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2   ,				cross_attention_dim=UpperCamelCase   ,				layers_per_block=1   ,				upcast_attention=UpperCamelCase   ,				use_linear_projection=UpperCamelCase   ,				)

     torch.manual_seed(0   )
     __a								=       DDIMScheduler(
         beta_schedule='scaled_linear'   ,				beta_start=0.00_085   ,				beta_end=0.012   ,				prediction_type='v_prediction'   ,				set_alpha_to_one=UpperCamelCase   ,				steps_offset=1   ,				)

     torch.manual_seed(0   )
     __a								=       AutoencoderKL()

     __a								=       {
         # image encoding components
         'feature_extractor': feature_extractor,
         'image_encoder': image_encoder.eval(),
         # image noising components
         'image_normalizer': image_normalizer.eval(),
         'image_noising_scheduler': image_noising_scheduler,
         # regular denoising components
         'tokenizer': tokenizer,
         'text_encoder': text_encoder.eval(),
         'unet': unet.eval(),
         'scheduler': scheduler,
         'vae': vae.eval(),
     }

     return components







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=0   ,				UpperCamelCase=True   )					->     Dict:
     if str(UpperCamelCase   ).startswith('mps'   ):
       __a								=       torch.manual_seed(UpperCamelCase   )
     else:
       __a								=       torch.Generator(device=UpperCamelCase   ).manual_seed(UpperCamelCase   )

     __a								=       floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(UpperCamelCase   )   ).to(UpperCamelCase   )

     if pil_image:
       __a								=       input_image * 0.5 + 0.5
       __a								=       input_image.clamp(0   ,				1   )
       __a								=       input_image.cpu().permute(0   ,				2   ,				3   ,				1   ).float().numpy()
       __a								=       DiffusionPipeline.numpy_to_pil(UpperCamelCase   )[0]

     return {
         "prompt": "An anime racoon running a marathon",
         "image": input_image,
         "generator": generator,
         "num_inference_steps": 2,
         "output_type": "np",
     }







   @skip_mps
   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       'cpu'  # ensure determinism for the device-dependent torch.Generator
     __a								=       self.get_dummy_components()
     __a								=       StableUnCLIPImgaImgPipeline(**UpperCamelCase   )
     __a								=       sd_pipe.to(UpperCamelCase   )
     sd_pipe.set_progress_bar_config(disable=UpperCamelCase   )

     __a								=       self.get_dummy_inputs(UpperCamelCase   )
     inputs.update({'image_embeds': None}   )
     __a								=       sd_pipe(**UpperCamelCase   ).images
     __a								=       image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     __a								=       np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       torch_device in ['cpu', 'mps']

     self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       torch_device in ['cpu', 'mps']

     self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase   )







   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()   ,				reason='XFormers attention is only available with CUDA and `xformers` installed'   ,				)
   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     self._test_xformers_attention_forwardGenerator_pass(test_max_difference=UpperCamelCase   )





@slow
@require_torch_gpu
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     __a								=       load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'   )

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-l-img2img'   ,				torch_dtype=torch.floataa   )
     pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       torch.Generator(device='cpu'   ).manual_seed(0   )
     __a								=       pipe(UpperCamelCase   ,				'anime turle'   ,				generator=UpperCamelCase   ,				output_type='np'   )

     __a								=       output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     __a								=       load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'   )

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-h-img2img'   ,				torch_dtype=torch.floataa   )
     pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       torch.Generator(device='cpu'   ).manual_seed(0   )
     __a								=       pipe(UpperCamelCase   ,				'anime turle'   ,				generator=UpperCamelCase   ,				output_type='np'   )

     __a								=       output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-h-img2img'   ,				torch_dtype=torch.floataa   )
     __a								=       pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       pipe(
         UpperCamelCase   ,				'anime turtle'   ,				num_inference_steps=2   ,				output_type='np'   ,				)

     __a								=       torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9



